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SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback

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Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation

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M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects

inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

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Layer-refined Graph Convolutional Networks for Recommendation

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Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation

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Graph Self-Supervised Learning: A Survey

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Self-Supervised Learning for Recommender Systems: A Survey

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A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

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Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

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Factorization meets the neighborhood

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Optimizing top-n collaborative filtering via dynamic negative item sampling

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Collaborative Filtering beyond the User-Item Matrix

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Graph Convolutional Neural Networks for Web-Scale Recommender Systems

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Deep Learning Based Recommender System

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Neural Graph Collaborative Filtering

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Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation

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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation

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Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

inferredcites →https://doi.org/10.1145/3477495openalex-snapshot

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Revisiting Negative Sampling vs. Non-sampling in Implicit Recommendation

inferredcites →https://doi.org/10.1145/3539597.3570409openalex-snapshot

Inductive Graph Transformer for Delivery Time Estimation

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Bootstrap Latent Representations for Multi-modal Recommendation

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A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation

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Backpropagation Applied to Handwritten Zip Code Recognition

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A survey on Image Data Augmentation for Deep Learning

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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation

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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

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Deep Learning based Recommender System: A Survey and New Perspectives

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Barlow Twins: Self-Supervised Learning via Redundancy Reduction

inferredcites →https://doi.org/10.48550/arxiv.2112.08679openalex-snapshot

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

inferredcites →https://doi.org/10.48550/arxiv.2303.11879openalex-snapshot

Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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